Quality Enhancement in Crowdsourcing

Crowdsourcing is an online, distributed problem-solving and production model. In a crowdsourcing environment, requesters dispense tasks (through some mechanisms) to the crowd workers to be solved in a limited time. As the workers in these markets are irregular and keep changing over time, it becomes a real challenge to designate a dropout. A crowd worker, staying out of the environment for a long period, may resume back at any time in future. There have been limited attempts toward dropout prediction in crowdsourcing markets, arguably due to the disagreement in defining a dropout. Controlling the dropout rate is an important issue for the quality enhancement in crowdsourcing markets. Apart from this, there are some other factors which can enhance quality of crowdsourcing platforms from different perspectives. One of these is the feedback mechanism. In fact, the feedback mechanism might have influence over controlling the dropout rate. In this paper, our objective is to find out the significance and impact of dropout prediction and feedback mechanism together on the quality enhancement of crowdsourcing markets.